AIエージェント
fast-agent
Code, build and evaluate LLM agents with full MCP, Skills, ACP and A2A support.
★ 3.9k スターApache-2.0
ガイドを見る →agnoは、agno-agiが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: agno-agi/agno）。GitHubスターは42,610、フォークは6,113で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
Build, run, and manage full agent platforms — not just single agents.
Know what you want the agent to accomplish before touching any code.
Clone the repo and read the README's requirements section.
Install what's needed and add API keys or configuration.
Run the quickstart example, then iterate from there.
Build, run, and manage — not just prototype.
Coordinate groups of agents, not lone wolves.
Aimed at running agents reliably, at scale.
Native feel for the Python AI ecosystem.
Wire agents to the tools they need.
Commercial-friendly licensing.
Download the source and move into the project folder.
git clone https://github.com/agno-agi/agno.git
cd agnoIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m agno # check README for the exact commandIt is an open-source project at agno-agi/agno with 6113 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — Apache 2.0 allows commercial use and modification. Keep the license and notice files, and note it includes a patent grant.
LangChain is a broad ecosystem; Agno focuses on the full agent-platform lifecycle — build, run, manage — in one Python framework.
That's the pitch: running and managing agent platforms, not just prototyping them.
Clone the repo and follow the README quickstart — the docs at https://docs.agno.com walk through setup.
Code, build and evaluate LLM agents with full MCP, Skills, ACP and A2A support.
Build and run agents you can see, understand and trust — framework plus a visual agent service
Build, run, and scale AI agents like APIs and microservices.
An MCP server and CLI that lets AI agents build, test and run iOS and macOS projects.
Chinese Java course: build AI agents with Spring AI, RAG, MCP, and tool calling.
Autonomous AI agents for penetration testing — self-hosted, multi-agent, with a full web UI
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — agnoが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でagnoとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
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